DP-900 Describe an analytics workload on Azure Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✓

  Azure Stream Analytics

  Why this is correct

  Azure Stream Analytics is a fully managed stream-processing engine that ingests events directly from Azure Event Hubs or IoT Hub, applies SQL-like queries with temporal windows such as tumbling, hopping, and sliding, and emits results to Blob Storage or other sinks. This gives the marketing team near-real-time sentiment analytics without provisioning clusters or writing distributed-streaming logic. Its low-latency, event-time-aware processing is exactly what this social media sentiment scenario requires.

• ✗

  Azure Data Factory

  Why it's wrong here

  Azure Data Factory is a data-integration and orchestration service designed for batch pipelines, using activities like Copy, Stored Proc, and custom .NET/Spark, often on a scheduled trigger rather than continuous event arrival. It can move batches of sentiment data to storage, but it does not perform per-event or windowed stream processing with second-level latency from Event Hubs. Relying on it would introduce significant delay and require additional components to simulate streaming, making it unsuitable for near-real-time analysis.

  When this WOULD be correct

  A question where the requirement is to orchestrate and schedule batch data movement from Event Hubs to Blob Storage, or to transform data in a batch manner using mapping data flows, without needing real-time processing.

• ✗

  Azure Databricks

  Why it's wrong here

  Azure Databricks can run Spark Structured Streaming to process incoming tweets, but it demands an active compute cluster, configuration of checkpoint directories and schema management, plus development in Python, Scala, or SQL. For a simple recurring sentiment aggregation, this is unnecessarily heavy—overhead in both cost and complexity—and a senior engineer would need to manually handle exactly-once and event-time processing. Stream Analytics provides the same capability with a declarative query and has no infrastructure to manage, making it the pragmatic choice here.

  When this WOULD be correct

  A question where the team needs to perform complex, custom machine learning transformations on streaming data (e.g., using a trained sentiment model in Python) and also requires collaborative notebook development. For example: 'A data science team wants to build and deploy a custom sentiment analysis model on streaming tweets, with the ability to iterate on the model interactively.'

• ✗

  Azure Synapse Analytics

  Why it's wrong here

  Azure Synapse Analytics is a cloud-scale analytics platform that combines dedicated SQL pools, serverless SQL, and Apache Spark for querying and processing large volumes of data, but it is not a real-time event processor. While Synapse can consume data from Event Hubs via a Spark notebook or SQL external table, doing so requires explicit code, checkpointing, and stream coordination, and its primary engines were built for interactive batch queries and workloads. This makes Synapse a valuable downstream store for aggregated results, not the service that should calculate the sentiment windows.

  When this WOULD be correct

  A question asks: 'You need to run complex T-SQL queries on large datasets stored in Azure Blob Storage and create a reporting dashboard. Which service provides a serverless SQL pool for querying data lakes?' In that scenario, Azure Synapse Analytics is correct.

Option-by-option analysis

Why each answer is right or wrong

Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The DP-900 exam frequently reuses these exact scenarios with slightly different constraints.

Analysis generated from the official DP-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”

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